.. _example_cluster_plot_cluster_comparison.py:
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Comparing different clustering algorithms on toy datasets
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This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last dataset is an example of a 'null'
situation for clustering: the data is homogeneous, and
there is no good clustering.
While these examples give some intuition about the algorithms,
this intuition might not apply to very high dimensional data.
The results could be improved by tweaking the parameters for
each clustering strategy, for instance setting the number of
clusters for the methods that needs this parameter
specified. Note that affinity propagation has a tendency to
create many clusters. Thus in this example its two parameters
(damping and per-point preference) were set to to mitigate this
behavior.
.. image:: images/plot_cluster_comparison_001.png
:align: center
**Python source code:** :download:`plot_cluster_comparison.py `
.. literalinclude:: plot_cluster_comparison.py
:lines: 23-
**Total running time of the example:** 24.88 seconds
( 0 minutes 24.88 seconds)